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  1. CSCE 580Artificial IntelligenceIntroduction and Ch.1 [P] Fall 2012 Marco Valtorta mgv@cse.sc.edu

  2. Catalog Description and Textbook • 580—Artificial Intelligence. (3) (Prereq: CSCE 350) Heuristic problem solving, theorem proving, and knowledge representation, including the use of appropriate programming languages and tools. • David Poole and Alan Mackworth. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press, 2010. [P] • Supplementary materials from the authors, including an errata list, are available • The full text is available online from the authors, in html format

  3. Course Objectives • Analyze and categorize software intelligent agents and the environments in which they operate • Formalize computational problems in the state-space search approach and apply search algorithms (especially A*) to solve them • Represent domain knowledge using features and constraints and solve the resulting constraint processing problems • Represent domain knowledge about objects using propositions and solve the resulting propositional logic problems using deduction and abduction • Represent knowledge in Horn clause form and use the AILog dialect of Prolog for reasoning • Reason under uncertainty using Bayesian networks • Represent domain knowledge about individuals and relations in first-order logic • Do inference using resolution refutation theorem proving (if time allows)

  4. Acknowledgment • The slides are based on the draft textbook and other sources, including other fine textbooks • The other textbooks I considered are: • David Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice-Hall, 2010 ( [AIMA] or [R] or [AIMA-1], [AIMA-2], and [AIMA-3], when distinguishing editions; the first and second editions were published in 1995 and 2003, respectively.) • Supplementary materials from the authors, including an errata list, are available online • Ivan Bratko. Prolog Programming for Artificial Intelligence, Third Edition. Addison-Wesley, 2001 • George F. Luger. Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Sixth Edition. Addison-Wesley, 2009

  5. Why Study Artificial Intelligence? • It is exciting, in a way that many other subareas of computer science are not • It has a strong experimental component • It is a new science under development • It has a place for theory and practice • It has a different methodology • It leads to advances that are picked up in other areas of computer science • Intelligent agents are becoming ubiquitous

  6. What is AI? Richard Bellman (1920-84) Aristotle (384BC -322BC) Thomas Bayes (1702-1761) Alan Turing (1912-1954)

  7. Acting Humanly: the Turing Test • Operational test for intelligent behavior: the Imitation Game • In 1950, Turing • predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes • Anticipated all major arguments against AI in following 50 years • Suggested major components of AI: knowledge, reasoning, language understanding, learning • Problem: Turing test is not reproducible, constructive, or amenable to mathematical analysis

  8. Thinking Humanly: Cognitive Science • 1960s “cognitive revolution": information-processing psychology replaced the prevailing orthodoxy of behaviorism • Requires scientific theories of internal activities of the brain • What level of abstraction? “Knowledge" or “circuits"? • How to validate? Requires • Predicting and testing behavior of human subjects (top-down), or • Direct identification from neurological data (bottom-up) • Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI • Both share with AI the following characteristic: • the available theories do not explain (or engender) anything resembling human-level general intelligence • Hence, all three fields share one principal direction!

  9. Thinking Rationally: Laws of Thought • Normative (or prescriptive) rather than descriptive • Aristotle: what are correct arguments/thought processes? • Several Greek schools developed various forms of logic: • notation and rules of derivation for thoughts; • may or may not have proceeded to the idea of mechanization • Direct line through mathematics and philosophy to modern AI • Problems: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have out of all the thoughts (logical or otherwise) that I could have? The Antikythera mechanism, a clockwork-like assemblage discovered in 1901 by Greek sponge divers off the Greek island of Antikythera, between Kythera and Crete.

  10. Acting Rationally • Rational behavior: doing the right thing • The right thing: that which is expected to maximize goal achievement, given the available information • Doesn't necessarily involve thinking (e.g., blinking reflex) but • thinking should be in the service of rational action • Aristotle (Nicomachean Ethics): • Every art and every inquiry, and similarly every action and pursuit, is thought to aim at some good

  11. Summary of IJCAI-83 Survey Attempt (A) 20.8 to Build (B) 12.8 Simulate (C) 17.6 Model (D) 17.6 that Machines (E) 22.4 Human (or People) (F) 60.8 Intelligent (G) 54.4 Behavior (I) 32.0 Processes (H) 24.0 by means of Computers (L) 38.4 Programs (M) 13.2

  12. A Detailed Definition [P] • Artificial intelligence, or AI, is the synthesis and analysis of computational agents that act intelligently • An agent is something that acts in an environment • An agent acts intelligently when: • what it does is appropriate for its circumstances and its goals • it is flexible to changing environments and changing goals • it learns from experience • it makes appropriate choices given its perceptual and computational limitations. An agent typically cannot observe the state of the world directly; it has only a finite memory and does not have unlimited time to act. • A computational agent is an agent whose decisions about its actions can be explained in terms of computation

  13. Some Comments on the Definition • A computational agent is an agent whose decisions about its actions can be explained in terms of computation • The central scientific goal of artificial intelligence is to understand the principles that make intelligent behavior possible in natural or artificial systems. This is done by • the analysis of natural and artificial agents • formulating and testing hypotheses about what it takes to construct intelligent agents • designing, building, and experimenting with computational systems that perform tasks commonly viewed as requiring intelligence • The central engineering goal of artificial intelligence is the design and synthesis of useful, intelligent artifacts. We actually want to build agents that act intelligently • We are interested in intelligent thought only as far as it leads to better performance

  14. A Map of the Field This course: • History, etc. • Problem-solving • Blind and heuristic search • Constraint satisfaction • Games (maybe) • Knowledge and reasoning • Propositional logic • First-order logic • Knowledge representation • Learning from observations (maybe) • A bit of reasoning under uncertainty Other courses: • Robotics (574) • Bayesian networks and decision diagrams (582) • Knowledge representation (780) or Knowledge systems (781) • Machine learning (883) • Computer graphics, text processing, visualization, image processing, pattern recognition, data mining, multiagent systems, neural information processing, computer vision, fuzzy logic; more?

  15. AI Prehistory • Philosophy • logic, methods of reasoning • mind as physical system • foundations of learning, language, rationality • Mathematics • formal representation and proof • algorithms, computation, (un)decidability, (in)tractability • Probability • Psychology • adaptation • phenomena of perception and motor control • experimental techniques (psychophysics, etc.) • Economics • formal theory of rational decisions • Linguistics • knowledge representation • Grammar • Neuroscience • plastic physical substrate for mental activity • Control Theory • homeostatic systems, stability • simple optimal agent designs

  16. Intellectual Issues in the Early History of AI (to 1982) 1965-80 Search versus Knowledge: Apparent paradigm shift within AI 1965-75 Power versus Generality: Shift of tasks of interest 1965- Competence versus Performance: Splits linguistics from AI and psychology 1965-75 Memory versus Processing: Splits cognitive psychology from AI 1965-75 Problem-Solving versus Recognition #2: Recognition rejoins AI via robotics 1965-75 Syntax versus Semantics: Splits lmyistics from AI 1965- Theorem-Probing versus Problem-Solving: Divides AI 1965- Engineering versus Science: divides computer science, incl. AI 1970-80 Language versus Tasks: Natural language becomes central 1970-80 Procedural versus Declarative Representation: Shift from theorem-proving 1970-80 Frames versus Atoms: Shift to holistic representations 1970- Reason versus Emotion and Feeling #2: Splits AI from philosophy of mind 1975- Toy versus Real Tasks: Shift to applications 1975- Serial versus Parallel #2: Distributed AI (Hearsay-like systems) 1975- Performance versus Learning #2: Resurgence (production systems) 1975- Psychology versus Neuroscience #2: New link to neuroscience 1980- - Serial versus Parallel #3: New attempt at neural systems 1980- Problem-solving versus Recognition #3: Return of robotics 1980- Procedural versus Declarative Representation #2: PROLOG 1640-1945 Mechanism versus Teleology: Settled with cybernetics 1800-1920 Natural Biology versus Vitalism: Establishes the body as a machine 1870- Reason versus Emotion and Feeling #1: Separates machines from men 1870-1910 Philosophy versus Science of Mind: Separates psychology from philosophy 1900-45 Logic versus Psychology: Separates logic from psychology 1940-70 Analog versus Digital: Creates computer science 1955-65 Symbols versus Numbers: Isolates AI within computer science 1955- Symbolic versus Continuous Systems: Splits AI from cybernetics 1955-65 Problem-Solving versus Recognition #1: Splits AI from pattern recognition 1955-65 Psychology versus Neurophysiology #1: Splits AI from cybernetics 1955-65 Performance versus Learning #1: Splits AI from pattern recognition 1955-65 Serial versus Parallel #1: Coordinate with above four issues 1955-65 Heuristics Venus Algorithms: Isolates AI within computer science 1955-85 Interpretation versus Compilation #1: Isolates AI within computer science 1955- Simulation versus Engineering Analysis: Divides AI 1960- Replacing versus Helping Humans: Isolates AI 1960- Epistemology versus Heuristics: divides AI (minor), connects with philosophy

  17. Programming Methodologies and Languages for AI Current use 33: Java28: Prolog28: Lisp or Scheme20: C, C# or C++16: Python7: Other Methodology: Run-Understand-Debug-Edit Languages: Spring 2008 survey Future use 38: Python33: Java27: Lisp or Scheme26: Prolog18: C, C# or C++13: Other

  18. Central Hypotheses of AI • A symbol is a meaningful pattern that can be manipulated (e.g., a written word, a sequence of bits). A symbol system creates, copies, modifies, and destroys symbols. • Symbol-system hypothesis: • A physical symbol system has the necessary and sufficient means for general intelligent action • Attributed to Allan Newell (1927-1992) and Herbert Simon (1916-2001) • Church-Turing thesis: • Any symbol manipulation can be carried out on a Turing machine • Alonzo Church (1903-1995) • Alan Turing (1912-1954) • The manipulation of symbols to produce action is called reasoning

  19. Agents and Environments

  20. Example Agent: Robot • actions: • movement, grippers, speech, facial expressions,. . . • observations: • vision, sonar, sound, speech recognition, gesture recognition,. . . • goals: • deliver food, rescue people, score goals, explore,. . . • past experiences: • effect of steering, slipperiness, how people move,. . . • prior knowledge: • what is important feature, categories of objects, what a sensor tell us,. . .

  21. Example Agent: Teacher • actions: • present new concept, drill, give test, explain concept,. . . • observations: • test results, facial expressions, errors, focus,. . . • goals: • particular knowledge, skills, inquisitiveness, social skills,. . . • past experiences: • prior test results, effects of teaching strategies, . . . • prior knowledge: • subject material, teaching strategies,. . .

  22. Example agent: Medical Doctor • actions: • operate, test, prescribe drugs, explain instructions,. . . • observations: • verbal symptoms, test results, visual appearance. . . • goals: • remove disease, relieve pain, increase life expectancy, reduce costs,. . . • past experiences: • treatment outcomes, effects of drugs, test results given symptoms. . . • prior knowledge: • possible diseases, symptoms, possible causal relationships. . .

  23. Example Agent: User Interface • actions: • present information, ask user, find another information source, filter information, interrupt,. . . • observations: • users request, information retrieved, user feedback, facial expressions. . . • goals: • present information, maximize useful information, minimize irrelevant information, privacy,. . . • past experiences: • effect of presentation modes, reliability of information sources,. . . • prior knowledge: • information sources, presentation modalities. . .

  24. The Role of Representation • Choosing a representation involves balancing conflicting objectives • Different tasks require different representations • Representations should be expressive (epistemologically adequate) and efficient (heuristically adequate)

  25. Desiderata of Representations • We want a representation to be • rich enough to express the knowledge needed to solve the problem • Epistemologically adequate • as close to the problem as possible: compact, natural and maintainable • amenable to efficient computation: able to express features of the problem we can exploit for computational gain • Heuristically adequate • learnable from data and past experiences • able to trade off accuracy and computation time

  26. Dimensions of Complexity • Modularity: • Flat, modular, or hierarchical • Representation: • Explicit states or features or objects and relations • Planning Horizon: • Static or finite stage or indefinite stage or infinite stage • Sensing Uncertainty: • Fully observable or partially observable • Process Uncertainty: • Deterministic or stochastic dynamics • Preference Dimension: • Goals or complex preferences • Number of agents: • Single-agent or multiple agents • Learning: • Knowledge is given or knowledge is learned from experience • Computational Limitations: • Perfect rationality or bounded rationality

  27. Modularity • You can model the system at one level of abstraction: flat • [P] distinguishes flat (no organizational structure) from modular (interacting modules that can be understood on their own; hierarchical seems to be a special case of modular) • You can model the system at multiple levels of abstraction: hierarchical • Example: Planning a trip from here to a resort in Cancun, Mexico • Flat representations are ok for simple systems, but complex biological systems, computer systems, organizations are all hierarchical • A flat description is either continuous or discrete. • Hierarchical reasoning is often a hybrid of continuous and discrete

  28. Succinctness and Expressiveness of Representations • Much of modern AI is about finding compact representations and exploiting that compactness for computational gains. • An agent can reason in terms of: • explicit states • features or propositions • It is often more natural to describe states in terms of features • 30 binary features can represent 230 = 1,073,741,824 states. • individuals and relations • There is a feature for each relationship on each tuple of individuals. • Often we can reason without knowing the individuals or when there are infinitely many individuals

  29. Example: States Thermostat for a heater • 2 belief (i.e., internal) states: off, heating • 3 environment (i.e., external) states: cold, comfortable, hot • 6 total states corresponding to the different combinations of belief and environment states

  30. Example: Features or Propositions Character recognition • Input is a binary image which is a 30x30 grid of pixels • Action is to determine which of the letters {a…z} is drawn in the image • There are 2900 different states of the image, and so 262900 different functions from the image state into the letters • We cannot even represent such functions in terms of the state space • Instead, we define features of the image, such as line segments, and define the function from images to characters in terms of these features

  31. Example: Relational Descriptions University Registrar Agent • Propositional description: • “passed” feature for every student-course pair that depends on the grade feature for that pair • Relational description: • individual students and courses • relations grade and passed • Define how “passed” depends on grade once, and apply it for each student and course. Moreover this can be done before you know of any of the individuals, and so before you know the value of any of the features covers_core_courses(St, Dept) <- core_courses(Dept, CC, MinPass) & passed_each(CC, St, MinPass). passed(St, C, MinPass) <- grade(St, C, Gr) & Gr >= MinPass.

  32. Planning Horizon How far the agent looks into the future when deciding what to do • Static: world does not change • Finite stage: agent reasons about a fixed finite number of time steps • Indefinite stage: agent is reasoning about finite, but not predetermined, number of time steps • Infinite stage: the agent plans for going on forever (process oriented)

  33. Uncertainty • There are two dimensions for uncertainty • Sensing uncertainty • Process uncertainty • In each dimension we can have • no uncertainty: the agent knows which world is true • disjunctive uncertainty: there is a set of worlds that are possible • probabilistic uncertainty: a probability distribution over the worlds

  34. Uncertainty • Sensing uncertainty: Can the agent determine the state from the observations? • Fully observable: the agent knows the state of the world from the observations. • Partially observable: many states are possible given an observation. • Process uncertainty: If the agent knew the initial state and the action, could it predict the resulting state? • Deterministic dynamics: the state resulting from carrying out an action in state is determined from the action and the state • Stochastic dynamics: there is uncertainty over the states resulting from executing a given action in a given state.

  35. Preference • Achievement goal is a goal to achieve. This can be a complex logical formula • Complex preferences may involve tradeoffs between various desiderata, perhaps at different times • ordinal only the order matters • cardinal absolute values also matter • Examples: coffee delivery robot, medical doctor

  36. Number of Agents • Single agent reasoning is where an agent assumes that any other agents are part of the environment • Multiple agent reasoning is when an agent reasons strategically about the reasoning of other agents • Agents can have their own goals: cooperative, competitive, or goals can be independent of each other

  37. Learning • Knowledge may be • given • learned (from data or past experience)

  38. Bounded Rationality Solution quality as a function of time for an anytime algorithm

  39. Examples of Representational Frameworks • State-space search • Classical planning • Influence diagrams • Decision-theoretic planning • Reinforcement Learning

  40. State-Space Search • flat or hierarchical • explicit states or features or objects and relations • static or finite stage or indefinite stage or infinite stage • fully observable or partially observable • deterministic or stochastic actions • goals or complex preferences • single agent or multiple agents • knowledge is given or learned • perfect rationality or bounded rationality

  41. Classical Planning • flat or hierarchical • explicit states or features or objects and relations • static or finite stage or indefinite stage or infinite stage • fully observable or partially observable • deterministic or stochastic actions • goals or complex preferences • single agent or multiple agents • knowledge is given or learned • perfect rationality or bounded rationality

  42. Influence Diagrams • flat or hierarchical • explicit states or features or objects and relations • static or finite stage or indefinite stage or infinite stage • fully observable or partially observable • deterministic or stochastic actions • goals or complex preferences • single agent or multiple agents • knowledge is given or learned • perfect rationality or bounded rationality

  43. Decision-Theoretic Planning • flat or hierarchical • explicit states or features or objects and relations • static or finite stage or indefinite stage or infinite stage • fully observable or partially observable • deterministic or stochastic actions • goals or complex preferences • single agent or multiple agents • knowledge is given or learned • perfect rationality or bounded rationality

  44. Reinforcement Learning • flat or hierarchical • explicit states or features or objects and relations • static or finite stage or indefinite stage or infinite stage • fully observable or partially observable • deterministic or stochastic actions • goals or complex preferences • single agent or multiple agents • knowledge is given or learned • perfect rationality or bounded rationality

  45. Comparison of Some Representations

  46. Four Application Domains • Autonomous delivery robot roams around an office environment and delivers coffee, parcels, etc. • Diagnostic assistant helps a human troubleshoot problems and suggests repairs or treatments • E.g., electrical problems, medical diagnosis • Intelligent tutoring system teaches students in some subject area • Trading agent buys goods and services on your behalf

  47. Environment for Delivery Robot

  48. Example inputs: Prior knowledge: its capabilities, objects it may encounter, maps. Past experience: which actions are useful and when, what objects are there, how its actions affect its position Goals: what it needs to deliver and when, tradeoffs between acting quickly and acting safely Observations: about its environment from cameras, sonar, sound, laser range finders, or keyboards Sample activities: Determine where Craig's office is. Where coffee is, etc. Find a path between locations Plan how to carry out multiple tasks Make default assumptions about where Craig is Make tradeoffs under uncertainty: should it go near the stairs? Learn from experience. Sense the world, avoid obstacles, pickup and put down coffee Autonomous Delivery Robot

  49. Environment for Diagnostic Assistant